% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RPS.R
\name{RPS}
\alias{RPS}
\title{Compute the Ranked Probability Score}
\usage{
RPS(
  exp,
  obs,
  time_dim = "sdate",
  memb_dim = "member",
  dat_dim = NULL,
  prob_thresholds = c(1/3, 2/3),
  indices_for_clim = NULL,
  Fair = FALSE,
  weights = NULL,
  cross.val = FALSE,
  ncores = NULL
)
}
\arguments{
\item{exp}{A named numerical array of the forecast with at least time and
member dimension.}

\item{obs}{A named numerical array of the observation with at least time 
dimension. The dimensions must be the same as 'exp' except 'memb_dim' and 
'dat_dim'.}

\item{time_dim}{A character string indicating the name of the time dimension.
The default value is 'sdate'.}

\item{memb_dim}{A character string indicating the name of the member dimension
to compute the probabilities of the forecast. The default value is 'member'.}

\item{dat_dim}{A character string indicating the name of dataset dimension. 
The length of this dimension can be different between 'exp' and 'obs'. 
The default value is NULL.}

\item{prob_thresholds}{A numeric vector of the relative thresholds (from 0 to
1) between the categories. The default value is c(1/3, 2/3), which 
corresponds to tercile equiprobable categories.}

\item{indices_for_clim}{A vector of the indices to be taken along 'time_dim' 
for computing the thresholds between the probabilistic categories. If NULL,
the whole period is used. The default value is NULL.}

\item{Fair}{A logical indicating whether to compute the FairRPS (the 
potential RPS that the forecast would have with an infinite ensemble size).
The default value is FALSE.}

\item{weights}{A named numerical array of the weights for 'exp'. If 'dat_dim' 
is NULL, the dimension should include 'memb_dim' and 'time_dim'. Else, the 
dimension should also include 'dat_dim'. The default value is NULL. The 
ensemble should have at least 70 members or span at least 10 time steps and
have more than 45 members if consistency between the weighted and unweighted
methodologies is desired.}

\item{cross.val}{A logical indicating whether to compute the thresholds between 
probabilistic categories in cross-validation.
The default value is FALSE.}

\item{ncores}{An integer indicating the number of cores to use for parallel 
computation. The default value is NULL.}
}
\value{
A numerical array of RPS with dimensions c(nexp, nobs, the rest dimensions of 
'exp' except 'time_dim' and 'memb_dim' dimensions). nexp is the number of 
experiment (i.e., dat_dim in exp), and nobs is the number of observation 
(i.e., dat_dim in obs). If dat_dim is NULL, nexp and nobs are omitted.
}
\description{
The Ranked Probability Score (RPS; Wilks, 2011) is defined as the sum of the
squared differences between the cumulative forecast probabilities (computed 
from the ensemble members) and the observations (defined as 0% if the category
did not happen and 100% if it happened). It can be used to evaluate the skill 
of multi-categorical probabilistic forecasts. The RPS ranges between 0 
(perfect forecast) and n-1 (worst possible forecast), where n is the number of
categories. In the case of a forecast divided into two categories (the lowest
number of categories that a probabilistic forecast can have), the RPS 
corresponds to the Brier Score (BS; Wilks, 2011), therefore, ranges between 0 
and 1. If there is more than one dataset, RPS will be computed for each pair 
of exp and obs data.
}
\examples{
exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50))
obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50))
res <- RPS(exp = exp, obs = obs)

}
\references{
Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
}
